System And Method For ECG Data Classification For Use In Facilitating Diagnosis Of Cardiac Rhythm Disorders

ABSTRACT

A system and method for ECG data classification for use in facilitating diagnosis of cardiac rhythm disorders is provided. ECG data is obtained via an electrocardiography monitor shaped for placement on a patient&#39;s chest. The ECG data is divided into segments and noise detection analysis is applied to the ECG data segments. A noise classification or a valid classification is assigned to each segment of the ECG data. At least one ECG data segment assigned the noise classification and that includes ECG data that corresponds with feedback from the patient via the electrocardiography monitor is identified. The ECG data that corresponds with the patient feedback is removed from the identified ECG data segment with the noise classification. The ECG data segments assigned the noise classification are removed from further analysis.

CROSS-REFERENCE TO RELATED APPLICATION

This non-provisional patent application is a continuation of U.S. patentapplication Ser. No. 15/934,888, filed Mar. 23, 2018, pending, which isa continuation-in-part of U.S. Pat. No. 10,045,709, issued Aug. 14,2018, which is a continuation of U.S. Pat. No. 9,408,551, issued Aug. 9,2016, which is a continuation-in-part of U.S. Pat. No. 9,345,414, issuedMay 24, 2016, which is a continuation-in-part of U.S. Pat. No.9,408,545, issued Aug. 9, 2016, which is a continuation-in-part of U.S.Pat. No. 9,700,227, issued Jul. 11, 2017, which is acontinuation-in-part of U.S. Pat. No. 9,730,593, issued Aug. 15, 2017;and of which is also a continuation-in-part of U.S. Pat. No. 9,936,875,issued Apr. 10, 2018, which is a continuation of U.S. Pat. No.9,788,722, issued Oct. 17, 2017, which is a division of U.S. Pat. No.9,504,423, issued Nov. 29, 2016; and this non-provisional patentapplication further claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent application, Ser. No. 62/132,497, filed Mar. 12,2015, U.S. Provisional application, Ser. No. 61/882,403, filed Sep. 25,2013, and U.S. Provisional application, Ser. No. 62/591,715, filed Nov.28, 2017, the disclosures of which are incorporated by reference.

FIELD

This application relates in general to electrocardiographic monitoringand, in particular, to a system and method for ECG data classificationfor use in facilitating diagnosis of cardiac rhythm disorders with theaid of a digital computer.

BACKGROUND

An ECG procedure measures cardiac electrical potentials that can begraphed to visually depict the electrical activity of the heart overtime and allows physicians to diagnose cardiac function by visuallytracing the cutaneous electrical signals (action potentials) that aregenerated by the propagation of the transmembrane ionic currents thattrigger the depolarization of cardiac fibers. Conventionally, astandardized set format 12-lead configuration is used by an ECG machineto record cardiac electrical signals from well-established traditionalchest locations.

An ECG trace contains alphabetically-labeled waveform deflections,PQRSTU, that represent distinct features within the cyclic cardiacactivation sequence and that can be interpreted post-ECG recordation toderive heart rate and physiology and for use in medical diagnosis andtreatment. The P-wave represents atrial depolarization, which causesatrial contraction. The QRS-complex represents ventriculardepolarization and is the largest electrical signal of the heart. TheT-wave and U-wave represents ventricular repolarization. The T andU-waves are not usually used in diagnosing most cardiac rhythm disordersand are included for completeness.

Here, the focus is around the R-wave, which is often used as anabbreviation for the QRS-complex. When measuring the time between an R-Rinterval, one can get a beat-by-beat assessment of heart rate.Typically, the R-R interval span between successive R-waves, in a normalheart, is 600 milliseconds (ms) to 1000 ms (i.e., one second) long,which respectively corresponds to 100 to 60 beats per minute (bpm). Ifone further considers the R-R interval as occurring over time,beat-by-beat, detailed cardiac physiology data may be embedded toprovide information that allows a physician to understand, at a glance,the context of the associated ECG rhythm both before and after asuspected rhythm abnormality and can be of confirmational andcollaborative value in cardiac arrhythmia diagnosis and treatment.

Conventionally, the potential of R-R interval context has not been fullyrealized, partly due to the difficulty of presentation in a concise andeffective manner to physicians. For instance, routine ECGs are typicallydisplayed at an effective paper speed of 25 millimeters (mm) per second.A lower speed is not recommended because ECG graph resolution degradesat lower speeds and diagnostically-relevant features may be lost.Conversely, a half-hour ECG recording, progressing at 25 mm/s, resultsin 45 meters of ECG waveforms that, in printed form, is cumbersome and,in electronic display form, will require significant back and forthtoggling between pages of waveforms, as well as presenting voluminousdata transfer and data storage concerns. As a result, ECGs are less thanideal tools for diagnosing cardiac arrhythmia patterns that only becomeapparent over an extended time frame, such as 10 minutes or longer.

In addition to or in lieu of physician review, the R-wave data can beprovided to analysis software for identification of cardiac events andpatient diagnosis. However, such software generally operates best ondatasets that represent a few minutes to two days of ECG data. As newerECG monitoring devices enter the market, many include longer datarecording periods. Accordingly, such analysis programs are overloadedwith data, which can hinder effectiveness of the analysis, leading to anincorrect patient diagnosis.

Further, performing an analysis of data that includes large amounts ofnoise can affect accuracy of patient diagnosis. For instance, noise canmask portions of the ECG data, which can prevent analysis software or amedical professional from identifying patterns of cardiac events, aswell as lead to an incorrect diagnosis or lack of a diagnosis.

Therefore, a need remains for improving quality of ECG data, whilereducing amounts of the ECG data for analysis, including patientdiagnosis, by identifying and removing noise. Such noise can berecognized by differentiating regions of valid ECG data from noise usingan adaptive noise detector and the regions of noise can be removed fromthe valid ECG data.

SUMMARY

ECG data is recorded for a patient via an ECG ambulatory monitor over anextended period of time. The recorded ECG data is provided to anadaptive noise detector prior to differentiating valid ECG data fromnoise. The noise can appear as device-related events and lead to aninaccurate diagnosis. The ECG data identified as noise can be removedand the remaining valid ECG data is then provided to a medicalprofessional or analysis software for analysis and patient diagnosis.

Patent feedback received during recording can be helpful to identifycardiac events. For instance, patients are instructed to press a tactilefeedback button on the ECG ambulatory monitor when they feel discomfort,ill, or that a cardiac event may be occurring. However, pressure on thebutton can often appear as noise and may be removed. To prevent removalof the data associated with the button press, a window surrounding thebutton press is designated. Any ECG data classified as noise andoverlapping the window is removed from the noise classification andremains with the ECG data for analysis via software or a medicalprofessional.

One embodiment provides a system and method for ECG data classificationfor use in facilitating diagnosis of cardiac rhythm disorders. ECG datais obtained via an electrocardiography monitor shaped for placement on apatient's chest. The ECG data is divided into segments and noisedetection analysis is applied to the ECG data segments. A noiseclassification or a valid classification is assigned to each segment ofthe ECG data. At least one ECG data segment assigned the noiseclassification and that includes ECG data that corresponds with feedbackfrom the patient via the electrocardiography monitor is identified. TheECG data that corresponds with the patient feedback is removed from theidentified ECG data segment with the noise classification. The ECG datasegments assigned the noise classification are removed from furtheranalysis.

Still other embodiments will become readily apparent to those skilled inthe art from the following detailed description, wherein are describedembodiments by way of illustrating the best mode contemplated. As willbe realized, other and different embodiments are possible and theembodiments' several details are capable of modifications in variousobvious respects, including time and clustering of events, all withoutdeparting from their spirit and the scope.

Accordingly, the drawings and detailed description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph showing, by way of example, a single ECG waveform.

FIG. 2 is a graph showing, by way of example, a prior art Poincaré R-Rinterval plot.

FIG. 3 is a flow diagram showing a method for facilitating diagnosis ofcardiac rhythm disorders with the aid of a digital computer inaccordance with one embodiment.

FIG. 4 is a flow diagram showing a routine for constructing anddisplaying a diagnostic composite plot for use in the method of FIG. 3.

FIG. 5 is a flow diagram showing a routine for constructing anextended-duration R-R interval plot for use in the routine of FIG. 4.

FIG. 6 is a diagram showing, by way of example, a diagnostic compositeplot generated by the method of FIG. 3.

FIG. 7 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of sinus rhythm (SR) transitioninginto atrial fibrillation (AF).

FIG. 8 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of 3:1 atrial flutter (AFL)transitioning into SR.

FIG. 9 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of atrial trigeminy.

FIG. 10 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of maximum heart rate in an episodeof AF during exercise.

FIG. 11 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of SR transitioning into AFLtransitioning into AF.

FIG. 12 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of sinus tachycardia andpalpitations that occurred during exercise accompanied by a jump inheart rate.

FIG. 13 is a diagram showing, by way of example, a diagnostic compositeplot for facilitating the diagnosis of bradycardia.

FIG. 14 is a block diagram showing a system for facilitating diagnosisof cardiac rhythm disorders with the aid of a digital computer inaccordance with one embodiment.

FIG. 15 is a perspective view showing an ECG ambulatory monitor havingan extended wear electrode patch with a monitor recorder inserted.

FIG. 16 is a perspective view showing the monitor recorder of FIG. 15.

FIG. 17 is a perspective view showing the extended wear electrode patchof FIG. 15 without a monitor recorder inserted.

FIG. 18 is a bottom plan view of the monitor recorder of FIG. 15.

FIG. 19 is a flow diagram showing a method for ECG data classificationfor use in facilitating diagnosis of cardiac rhythm disorders with theaid of a digital computer.

FIG. 20 is a flow diagram showing a method for noise detection.

FIG. 21 is a flow diagram showing, by way of example, a method fortraining an adaptive noise detector.

FIG. 22 is a flow diagram showing, by way of example, a method forclassification of ECG data.

FIG. 23 is a flow diagram showing, by way of example, a method fortrimming data segments classified as noise.

FIG. 24 is a diagram showing, by way of example, a diagnostic compositeplot showing data from a button press window.

FIG. 25 is a diagram showing, by way of example, a summary graph of datapress occurrences.

DETAILED DESCRIPTION

A normal healthy cardiac cycle repeats through an expected sequence ofevents that can be visually traced through an ECG. Each cycle startswith cardiac depolarization originating high in the right atrium in thesinoatrial (SA) node before spreading leftward towards the left atriumand inferiorly towards the atrioventricular (AV) node. After a delay inthe AV node, the depolarization impulse transits the Bundle of His andmoves into the right and left bundle branches and Purkinje fibers toactivate the right and left ventricles.

When a rhythm disorder is suspected, diagnostically-relevant arrhythmicevents in the cardiac cycle can often be identified and evaluated withthe assistance of an ECG and R-R interval tachography, somewhat similarto Poincaré plots. Routine ECG evaluation is primarily focused onidentifying changes to expected ECG waveform shapes. R-R intervaltachographs are focused on showing heart rate trends prior to and afteran arrhythmia. This information is critical to understanding theunderpinnings of arrhythmia onset and offset, both features critical tomanaging the patient regardless of the specific arrhythmia shown on theECG. FIG. 1 is a graph showing, by way of example, a single ECG waveform10. The x-axis represents approximate time in units of tenths of asecond and the y-axis represents approximate cutaneous electrical signalstrength in units of millivolts. By long-standing convention, ECGs aretypically printed or displayed at an effective paper speed of 25millimeters (mm) per second. Although in practice an ECG may be providedto a physician in traditional paper-printed form, in “virtual”electronic display form, or both. Nevertheless, the term “effectivepaper speed” is still widely applied as a metric to normalize therecorded ECG signal to a standardized grid of 1 mm squares (omitted forthe sake of clarity in FIG. 1), whereby each 1 mm horizontal box in thegrid corresponds to 0.04 s (40 ms) of recorded time. Other effectivepaper speeds, grid sizes and units of display are possible.

A full ECG consists of a stream of alphabetically-labeled waveforms 10that collectively cover cardiac performance over a period ofobservation. For a healthy patient, within each ECG waveform 10, theP-wave 11 will normally have a smooth, normally upward, positivewaveform that indicates atrial depolarization. The QRS complex 17 willusually follow during normal rhythms, often with a downward deflectionof a Q-wave 12, followed by a larger upward deflection of an R-wave 13,and be terminated with a downward waveform of the S-wave 14, which arecollectively representative of ventricular depolarization. This term,QRS, is often reduced to the “R-wave”. Thus, reference to the R-Rinterval means the time from one R-wave (or QRS) to another R-wave. Forcompleteness sake of showing the full ECG, we identify the T-wave 15,which will normally be a modest upward waveform, representative ofventricular repolarization, while the U-wave 16, which is often notdirectly observable, will indicate the recovery period of the Purkinjeconduction fibers. For the purposes of the R-R plot, this part of theECG waveform will not be pertinent.

Rhythm disorders often manifest through R-R interval variability orthrough the patterns formed by R-R intervals over an extended timeperiod prior to and after a specific arrhythmia onset and offset. Bothare important tools in the diagnosis of cardiac rhythmamonibnormalities. For example, atrial fibrillation (AF) is the chaoticfiring of the atria that leads to an erratic activation of theventricles. AF is initially diagnosed by an absence of organized P-waves11 and confirmed by erratic ventricular rates that manifest in an ECGR-R interval plot as a cloud-like pattern of irregular R-R intervals dueto an abnormal conduction of impulses to the ventricles. There is aGaussian-like distribution to these R-R intervals during AF. Similarly,atrial flutter (AFL) is an abnormal heart rhythm in which cardiacimpulses travel along pathways within the right atrium in an organizedcircular motion, causing the atria to beat faster than and out of syncwith the ventricles. During AFL, the heart beats quickly, yet with aregular pattern. Although AFL presents in an ECG in a “sawtooth”pattern, AFL behavior can fluctuate wildly. Such fluctuation can beconfirmed in an ECG by characteristic R-R interval patterns that usuallymanifest as 2:1 atrioventricular (AV) conduction, 4:1 AV conduction oreven variable AV conduction, as it frequently can be. The importance ofthis latter manifestation of AFL relates to how AFL can be distinguishedfrom AF. The R-R interval plot can clearly distinguish the two unlikealgorithms that frequently conflate AF with AFL and variable conduction.

Conventionally, R-R intervals have been visualized using Poincare plots.FIG. 2 is a graph showing, by way of example, a prior art Poincare R-Rinterval plot 18. The x-axis represents the duration of R-R interval nin units of milliseconds (ms). The y-axis represents the duration of R-Rinterval n+1 also in units of ms. Ordinarily, the x- and y-axes use thesame units, so as to form a trend line 19 along the 45-degree angle.When an R-R interval is equal to the successive R-R interval, as oftenoccurs when heart rhythm is regular, the dot representing the twointervals falls onto the 45-degree trend line 19. Conversely, when anR-R interval has changed since the preceding R-R interval, the dotrepresenting the two intervals falls off the 45-degree trend line 19and, as the difference between successive R-R intervals increases, thedots fall further away from the trend line 19.

The number of dots deviating from the trend line 19 in a Poincare plotcan indicate the frequency of occurrence of irregular heartbeats whencompared to the number of dots on the trend line 19. The distance of thedots to the trend line 19 can approximate the extent of heart ratechange from one heartbeat to the next. However, as heart rate change islimited to only successively-occurring heartbeats, the linearity of timeand associated contextual information over an extended time frame arelost. Poincare plots can be generally predictive but they have limitedvalue when used to understand physiological antecedents to an arrhythmiapre- and post-rhythm events, for example something as simple asexercise. In addition, significant changes in heart rate, particularlyspikes in heart rate, such as due to sinus rhythm transitions to atrialflutter, may be masked, distorted or even omitted in a Poincare plot ifthe change occurs over non-successive heartbeats. In summary, a Poincareplot is more useful as a mathematical tool than a physiological one, andtherefore a Poincare plot cannot truly represent what the heart is doingserially over time with respect to changes in the heart's normal andabnormal physiology on a beat-by-beat basis.

Despite the limitations of Poincaré plots and related forms of R-Rinterval tachography, R-R interval data when presented in a formatduplicating temporal physiological events manifest on an actual ECGremains a key tool that physicians can rely upon to identifytemporally-related cardiac dysrhythmic patterns. Interpretation of R-Rinterval data can be assisted by including multiple temporal points ofreference and a plot of R-R interval data that comparatively depictsheart rate variability in concert with R-R interval data. FIG. 3 is aflow diagram showing a method 20 for facilitating diagnosis of cardiacrhythm disorders with the aid of a digital computer in accordance withone embodiment. The method 20 can be implemented in software andexecution of the software can be performed on a computer, such asfurther described infra with reference to FIG. 14, as a series ofprocesses or method modules or steps.

As a precursor step, the patient's ECG data are recorded over a set timeperiod (step 21), which can be over a short term or extended time frame.ECG recordation, as well as other physiological monitoring, can beprovided through various kinds of ECG-capable monitoring ensembles,including a standardized 12-lead ECG setup, such as used for clinicalECG monitoring, a portable Holter-type ECG recorder for traditionalambulatory ECG monitoring, or a wearable ambulatory ECG monitor, such asa flexible extended wear electrode patch and a removable reusable (orsingle use) monitor recorder, such as described in commonly-assignedU.S. Pat. No. 9,345,414, issued May 24, 2016, the disclosure of which isincorporated by reference. The wearable ambulatory ECG monitor includesan electrode patch and monitor recorder that are synergisticallyoptimized to capture electrical signals from the propagation of lowamplitude, relatively low frequency content cardiac action potentials,particularly the P-waves, generated during atrial activation, and isdescribed in further detail below with respect to FIG. 15. Still otherforms of ECG monitoring assemblies are possible.

Upon completion of the monitoring period, the ECG and any otherphysiological data are downloaded or retrieved into a digital computer,as further described infra with reference to FIG. 14, with, forinstance, the assistance of a download station or similar device, or viawireless connection, if so equipped, and a vector of the downloaded orretrieved ECG data is obtained (step 22). In one embodiment, the vectorof ECG data represents a 40-minute (or other duration) time span that isused in constructing the plot of R-R interval data, although otherpre-event and post-event time spans are possible. Optionally, apotentially-actionable cardiac event within the vector of ECG data canbe identified and the ECG data during, prior to and after the event isselected (step 23). The event could be identified with the assistance ofa software package, such as Holter LX Analysis Software, licensed byNorthEast Monitoring, Inc., Maynard, Mass.; Intelli Space CardiovascularImage and Information management system, licensed Koninklijke PhilipsN.V., Amsterdam, Netherlands; MoMe System, licensed by InfoBionic,Lowell, Mass.; Pyramis ECG Management, licensed by Mortara InstrumentInc., Milwaukee, Wis.; ICS Clinical Suite, licensed by SpacelabsHealthcare Inc., Snoqualmie, Wash.; or a customized software package.Alternatively, the potentially-actionable cardiac event could beidentified by a physician or technician during review of the ECG data.

To improve diagnosis of heart rate variability, a diagnostic compositeplot is constructed that includes one or more temporal points ofreference into the ECG data, which provide important diagnostic context,and a plot of R-R interval data is constructed based on the vector ofECG data (step 24), as further described infra with reference to FIG. 4.Briefly, both near field and far field contextual views of the ECG dataare constructed and displayed. Both views are temporally keyed to anextended duration R-R interval data view that, in one embodiment, isscaled non-linearly (for practical viewing reasons) to maximize thevisual differentiation for frequently-occurring heart rate ranges, suchthat a single glance allows the physician to make a diagnosis. The useof a non-linear heart rate scale adjacent to the ECG prevents a skewingof the R-R data to exceed the visual format of a page of paper. Thus,all three views are presented simultaneously, in a visually convenientformat that can present 3 sequential ECG data sets per page, therebyallowing the interpreting physician to diagnose rhythm and the pre- andpost-contextual events leading up to a cardiac rhythm of interest in areading and storage friendly manner.

In a further embodiment, findings made through interpretation of heartrate variability patterns in the diagnostic composite plot can beanalyzed to form a diagnosis of a cardiac rhythm disorder (step 25),such as the cardiac rhythm disorders listed, by way of example, inTable 1. For instance, the heart rate variability patterns in thediagnostic composite plot could be provided to a system thatprogrammatically detects AF by virtue of looking for the classicGaussian-type distribution on the “cloud” of heart rate variabilityformed in the plot of R-R interval data, which can be corroborated bythe accompanying contextual ECG data. Finally, therapy to addressdiagnosed disorder findings can optionally be programmed into a cardiacrhythm therapy delivery device (step 26), such as an implantable medicaldevice (IMD) (not shown), including a pacemaker, implantablecardioverter defibrillator (ICD), or similar devices.

TABLE 1 Cardiac Rhythm Disorders Normal sinus rhythm Sinus BradycardiaSinus Tachycardia Premature atrial and ventricular beats Ectopic atrialtachycardia Atrial fibrillation Atrial flutter Atrial or ventricularbigeminy, trigeminy or quadrigeminy Sinus Bradycardia Fusion beatsInterpolated ventricular premature beats Intraventricular conductiondelay Junctional rhythm AV Nodal re-entrant tachycardia AV re-entranttachycardia Wolff-Parkinson-White Syndrome and Pre-excitationVentricular tachycardia Accelerated idioventricular rhythm AV Wenckebachblock AV Type II block Sinoatrial block

A diagnostic composite plot is constructed and displayed to helpphysicians identify and diagnose temporally-related cardiac dysrhythmicpatterns. The diagnostic composite plot includes ECG traces from two ormore temporal points of reference and a plot of R-R interval data,although other configurations of ECG data plots when combined with theR-R interval plot will also provide critical information. FIG. 4 is aflow diagram showing a routine 30 for constructing and displaying adiagnostic composite plot for use in the method 20 of FIG. 3. Specificexamples of diagnostic composite plots are discussed in detail infrawith reference to FIGS. 7-13.

In the diagnostic composite plot, R-R interval data is presented tophysicians in a format that includes views of relevant near field andfar field ECG data, which together provide contextual information thatimproves diagnostic accuracy. In a further embodiment, other views ofECG data can be provided in addition to or in lieu of the near field andfar field ECG data views. The near field (or short duration) ECG dataprovides a “pinpoint” classical view of an ECG at traditional recordingspeed in a manner that is known to and widely embraced by physicians.This near field ECG data is coupled to a far field (or medium duration)ECG data view that provides an “intermediate” lower resolution, offeringpre- and post-event contextual ECG data. Thus, the extended-duration R-Rinterval plot is first constructed (step 31), as further described infrawith reference to FIG. 5. Optionally, noise can be filtered from the R-Rinterval plot (step 32), which is then displayed (step 33). In a furtherembodiment, noise can be filtered from the ECG data prior toconstructing the R-R interval plot, as further described below withreference to FIG. 19. Noise filtering can include low-pass or high-passfiltering or other forms of signal processing, including automatic gaincontrol, such as described in commonly-assigned U.S. Pat. No. 9,345,414,issued May 24, 2016, cited supra, as well as identification via anadaptive noise detector, as further described in detail below withrespect to FIGS. 19-25.

Rhythm disorders have different weightings depending upon the contextwith which they occur. In the diagnostic composite plot, the R-Rinterval data view and the multiple views of the ECG data around whichthe R-R data embrace, provide that necessary context. Effectively, theshort and medium duration ECG data that accompanies theextended-duration R-R interval plot represents the ECG data “zoomed” inaround a temporal point of reference identified in the center (or otherlocation) of the R-R interval plot, thereby providing a short-term,medium-term and long-term visual context to the physician that allowstemporal assessment of cardiac rhythm changes in various complementaryviews of the heart's behavior. The durations of the classical “pinpoint”view, the pre- and post-event “intermediate” view, and the R-R intervalplot are flexible and adjustable. In one embodiment, the diagnosticcomposite plot displays R-R interval data over a forty-minute durationand ECG data over short and medium durations (steps 34 and 35), such asfour-second and 24-second durations that provide two- and 12-secondsegments of the ECG data before and after the RR interval plot'stemporal point of reference, which is generally in the center of the R-Rinterval plot, although other locations in the R-R interval plot couldbe identified as the temporal point of reference. The pinpoint“snapshot” and intermediate views of ECG data with the extended term R-Rinterval data comparatively depicts heart rate context and patterns ofbehavior prior to and after a clinically meaningful arrhythmia orpatient concern, thereby enhancing diagnostic specificity of cardiacrhythm disorders and providing physiological context to improvediagnostic ability. In a further embodiment, diagnostically relevantcardiac events can be identified and the R-R interval plot can beconstructed with a cardiac event centered in the middle (or otherlocation) of the plot, which thereby allows pre- and post-event heartrhythm data to be contextually “framed” through the pinpoint andintermediate ECG data views. Other durations, intervals andpresentations of ECG data are possible, including allowing the physicianor viewer to select or scroll through the raw ECG data to advance theECG data together with the R-R interval data, allowing context to beselected by the viewer.

The extended-duration R-R interval plot presents beat-to-beat heart ratevariability in a format that is intuitive and contextual, yet condensed.The format of the R-R interval plot is selected to optimizevisualization of cardiac events in a compressed, yet understandablefield of view, that allows for compact presentation of the data akin toa cardiologists understanding of clinical events. FIG. 5 is a flowdiagram showing a routine 40 for constructing an extended-duration R-Rinterval plot for use in the routine 30 of FIG. 4. The duration of theR-R interval plot can vary from less than one minute to the entireduration of the recording. Thus, a plurality of R-wave peaks is firstselected out of the vector of ECG data (step 41) appropriate to theduration of the R-R interval plot to be constructed. For successivepairs of the R-wave peaks (steps 42-43), the difference between therecording times of the R-peaks is calculated (step 43). Each recordingtime difference represents the length of one heartbeat. The heart rateassociated with the recording time difference is determined by taking aninverse of the recording time difference and normalizing the inverse tobeats per minute (step 44). Taking the inverse of the recording timedifference yields a heart rate expressed in beats per second, which canbe adjusted by a factor of 60 to provide a heart rate expressed in bpm.Calculation of the differences between the recording times and theassociated heart rate continues for all of the remaining pairs of theR-wave peaks (step 44).

The pairings of R-R intervals and associated heart rates are formed intoa two-dimensional plot. R-R intervals are plotted along the x-axis andassociated heart rates are plotted along the y-axis. The range and scaleof the y-axis (heart rate) can be adjusted according to the range andfrequency of normal or patient-specific heart rates, so as to increasethe visual distinctions between the heart rates that correspond todifferent R-R intervals. In one embodiment, the y-axis of the R-Rinterval plot has a range of 20 to 300 beats per minute and R-Rintervals corresponding to heart rates falling extremely outside of thisrange are excluded to allow easy visualization of 99+% of the heart ratepossibilities. To encompass all heart rates theoretically possible, 0 to350 bpm, would alter the practical visibility of the interval range forthe vast majority of events. (Note that these extreme rates remainvisible in the ECG.)

In a further embodiment, they-axis has a non-linear scale that iscalculated as a function of the x-axis (R-R interval), such that:

$y = \left( \frac{x - {\min \mspace{11mu} {bpm}}}{{\max \mspace{11mu} {bpm}} - {\min \mspace{11mu} {bpm}}} \right)^{n}$

where x is the time difference, min bpm is the minimum heart rate, maxbpm is the maximum heart rate, and n<1. The non-linear scale of they-axis accentuates the spatial distance between successive heart rateswhen heart rate is low. For example, when n=2, the spatial differencebetween 50 and 60 bpm is 32% larger than the spatial difference between90 bpm and 100 bpm, and 68% larger than the spatial difference between150 bpm and 160 bpm. As a result the overall effect is to accentuate thespatial differences in frequently-occurring ranges of heart rate andde-emphasize the spatial differential in ranges of heart rate where adeviation from norm would have been apparent, thus maximizing thespatial efficiency in data presentation. The goal is to show cardiacevents in a simple, small visual contextual format. Larger scales andlarger formats bely the practical limits of single-page presentationsfor the easy visualization at a glance by the busy physician. The visualdistinctions between the heart rates that correspond to different R-Rintervals stand out, especially when plotted on a non-linear scale. Inthis configuration, three distinct arrhythmia events can be presented instandard 8.5 by 11 inch page formats. However, other formats arepossible. Also, other y-axis ranges and scales are possible as may beselected by distinct clinical needs and specific diagnosticrequirements.

The diagnostic composite plot includes a single, long range view of R-Rinterval data and a pair of pinpoint ECG data views that together helpto facilitate rhythm disorder diagnosis by placing focused long-termheart rate information alongside short-term and medium-term ECGinformation. Such pairing of ECG and R-R interval data is unique in itsability to inform the physician of events prior to, during and after acardiovascular event. FIG. 6 is a diagram showing, by way of example, adiagnostic composite plot 50 generated by the method 30 of FIG. 3. Notethat the diagnostic composite plot can be tailored to include more thanone view of R-R interval data and as many views of contextual ECG dataas needed. In a further embodiment, a background information plotpresenting an extended far field of related information can be included,such as activity amount, activity intensity, posture, syncope impulsedetection, respiratory rate, blood pressure, oxygen saturation (SpO₂),blood carbon dioxide level (pCO₂), glucose, lung wetness, andtemperature. Other forms of background information are possible. In astill further embodiment, background information can be layered on topof or keyed to the diagnostic composite plot 50, particularly at keypoints of time in the R-R interval data plot, so that the contextprovided by each item of background information can be readily accessedby the reviewing physician.

The diagnostic composite plot 50 includes an ECG plot presenting a nearfield (short duration) view 51, an ECG plot presenting an intermediatefield (medium duration) view 52, and an R-R interval data plotpresenting a far field (extended duration) view 53. The three views 51,52, 53 are juxtaposed alongside one other to allow quick back and forthreferencing of the full context of the heart's normal and abnormalphysiology. Typically, a temporal point of reference, which could be adiagnostically relevant cardiac event, patient concern or other indicia,would be identified and centered on the x-axis in all three views. Theplacement of the temporal point of reference in the middle of all threex-axes enables the ECG data to be temporally keyed to the R-R intervaldata appearing in the center 60 of the R-R interval data view 53, with anear field view 51 of an ECG displayed at normal (paper-based) recordingspeed and a far field view 52 that presents the ECG data occurringbefore and after the center 60. As a result, the near field view 51provides the ECG data corresponding to the R-R interval data at thecenter 60 (or other location) in a format that is familiar to allphysicians, while the intermediate field view 52 enables presentation ofthe broader ECG data context going beyond the borders of the near fieldview 51. In a further embodiment, the center 60 can be slidably adjustedbackwards and forwards in time, with the near field view 51 and the farfield view 52 of the ECG data automatically or manually adjustingaccordingly to stay in context with the R-R interval data view 51. In astill further embodiment, multiple temporal points of reference can beidentified with each temporal point of reference being optionallyaccompanied by one or more dedicated sets of ECG data views.

The collection of plots are conveniently arranged close enough to oneanother to facilitate printing on a single page of standard sized paper(or physical paper substitute, such as a PDF file), although otherlayouts of the plots are possible. The far field view 53 is plotted withtime in the x-axis and heart rate in the y-axis. The R-R intervals arecalculated by measuring the time occurring between successive R-wavepeaks. In one embodiment, the far field view 53 presents R-R intervaldata (expressed as heart rate in bpm) that begins about 20 minutes priorto and ends about 20 minutes following the center 60, although otherdurations are possible.

The near field view 51 and intermediate field view 52 present ECG datarelative to the center 60 of the far field view 53. The near field view51 provides a pinpoint or short duration view of the ECG data. In oneembodiment, the near field view 51 presents ECG data 55 that beginsabout two seconds prior to and ends about two seconds following thecenter 60, although other durations are possible. The intermediate fieldview 52 provides additional contextual ECG information allowing thephysician to assess the ECG itself and gather a broader view of therhythm before and after a “blow-up” of the specific arrhythmia ofinterest. In one embodiment, the intermediate field view 52 presents ECGdata 56 that begins about 12 seconds prior to and ends about 12 secondsfollowing the center 60, although other durations are possible. Forconvenience, the eight-second interval of the ECG data 56 in theintermediate field view 52 that makes up the ECG data 56 in the nearfield view 51 is visually highlighted, here, with a surrounding box 57.In addition, other views of the ECG data, either in addition to or inlieu of the near field view 51 and the far field view 52 are possible.Optionally, an ECG plot presenting an extended far field view 54 of thebackground information can be included in the diagnostic composite plot50. In one embodiment, the background information is presented asaverage heart rate with day and night periods 58 alternately shadedalong the x-axis. Other types of background information, such asactivity amount, activity intensity, posture, syncope impulse detection,respiratory rate, blood pressure, oxygen saturation (SpO₂), blood carbondioxide level (pCO₂), glucose, lung wetness, and temperature, arepossible.

Examples of the diagnostic composite plot as applied to specific formsof cardiac rhythm disorders will now be discussed. These examples helpto illustrate the distinctive weightings that accompany different formsof rhythm disorders and the R-R interval and ECG waveform deflectioncontext with which they occur. FIG. 7 is a diagram showing, by way ofexample, a diagnostic composite plot 70 for facilitating the diagnosisof sinus rhythm (SR) transitioning into AF. SR is indicated through thepresence of a reasonably steady baseline, but with subsidiary lines ofpremature beats and their compensatory pauses. SR manifests as ashadowing 71 of a high heart rate line and a low heart rate line. AF ischaracterized by irregular heartbeats with a somewhat random variationof R-R intervals, although within a limited range and concentrating in aGaussian-like distribution pattern around a mean that varies over time.Such characteristics can also be distinct from the more random nature ofnoise. Although AF can be diagnosed by viewing a near field view 51 ofECG data showing heartbeats with reversed P-wave and irregular R-Rintervals, this approach may be unclear when viewing “snippets” of ECGdata, especially when associated with poor quality ECG signals. Thepresence of AF can also be confirmed through a far field view 53 of R-Rinterval data, in which the R-R intervals assume superficially appearingdisorganized, spread-out and decentralized scattered cloud 72 along thex-axis, in comparison to a concentrated, darkened line typical of a moreorganized cardiac rhythm.

FIG. 8 is a diagram showing, by way of example, a diagnostic compositeplot 80 for facilitating the diagnosis of 3:1 atrial flutter (AFL)transitioning into SR with frequent premature ectopic atrial beats. Inthe initial part of the R-R interval plot, the R-R intervals have adiscernible aggregated line in the middle of the cloud 81 when therhythm has yet to stabilize into a set pattern, not quite AF and notquite AFL. Immediately thereafter, a dense line representing firm 3:1atrial flutter stabilizes the rhythm prior to the transition into SRassociated with the presence of two seesawing baselines that result fromfrequent atrial ectopy causing short coupling intervals and thencompensatory long coupling intervals. SR is indicated by the middle ofthe three lines with a low heart rate line consistent with thecompensatory pause (long coupling interval) and a high heart rate linewith the shortest coupling interval representing the series of atrialpremature beats 82, and thus, at a faster heart rate.

FIG. 9 is a diagram showing, by way of example, a diagnostic compositeplot 90 for facilitating the diagnosis of atrial trigeminy. Atrialtrigeminy is characterized by three heartbeat rates appearingintermittently yet reasonably regularly. Although atrial trigeminy canbe diagnosed by viewing a near field view 51 of ECG data, the patternand its clinical impact and frequency is significantly more recognizablein a far field view 53 of R-R interval data, in which a repeatingpattern of three distinct heartbeat lines are persistently present andclearly visible 91. This view also provides the physician with aqualitative feel for the frequency of the event troubling the patientthat is not discernible from a single ECG strip.

FIG. 10 is a diagram showing, by way of example, a diagnostic compositeplot 100 for facilitating the diagnosis of maximum heart rate in anepisode of AF during exercise. In a far field view 50 of R-R intervaldata, AF manifests through a dispersed cloud of dots (Gaussian-likedistribution) without a discernible main heart rate line representingregular heartbeats 101. Under exercise, the maximum heartbeat can belocated by an increase in heart rate clustered about the cloud 102. Inaddition, individual dots above the 200 bpm range throughout the entire40-minute range indicates the maximum heart rate during exercise. Thevery rapid rise in heart rate can be critical to patient management, assuch bumps in rate by exercise can prove dangerous and even triggercardiac arrest by inducing ventricular fibrillation. Their very presenceis easily visualized in the R-R interval data plot, thereby allowing thephysician to alter therapy sufficiently to control such potentiallydamaging rises in heart rate.

FIG. 11 is a diagram showing, by way of example, a diagnostic compositeplot 110 for facilitating the diagnosis of SR transitioning into AFLtransitioning into AF. In a far field view 53 of R-R interval data, SRmanifests as an uneven main heart rate line with a fluctuating height111. At the onset of AFL, the main heart rate line breaks away at alower heart rate than the SR main heart rate line 112. The episode ofAFL further evolves into AF as characterized by a dispersed cloud ofirregular heartbeats without concentrated heart rate lines 113. Thisview provides critical information to the physician managing AF patientsin that, at a glance, the view provides data that tells the physicianthat the patient's AF may be the consequence of AFL. Such knowledge mayalter both drug and procedure therapies, like catheter ablation detailsof intervention. These two disorders are not interchangeable andgenerally demand different drug and different catheter ablationapproaches.

FIG. 12 is a diagram showing, by way of example, a diagnostic compositeplot 120 for facilitating the diagnosis of sinus tachycardia andpalpitations that occurred during exercise accompanied by a jump inheart rate. In a far field view 50 of R-R interval data, sinustachycardia is indicated by the presence of a baseline heart rate ofabout 60 bpm 121 that spikes up to around 100 bpm 122 and graduallyslopes down with a wide tail 123, reflecting a sharp rise of heart ratesfollowed by a gradual decline. The associated ECG data in the near fieldand intermediate field views (not shown) can confirm the rhythm as sinusrhythm and a normal response to exercise. This rhythm, althoughsuperficially obvious, was associated with symptoms of palpitations anddemonstrates a sensitivity to heart rate fluctuations, rather than asensitivity to an arrhythmia. This common problem is often dismissed asmerely sinus tachycardia, rather than recognizing the context of achanging rate that generated the patient's complaint, a problem, visibleonly in the R-R interval data plot.

FIG. 13 is a diagram showing, by way of example, a diagnostic compositeplot 90 for facilitating the diagnosis of bradycardia during sleep and aR-R interval pattern characteristic of sleep. Bradycardia refers to aresting heart rate of under 60 bpm. Bradycardia during sleep is oftentempered with occasional spikes of rapid heart rate, which can be asecondary compensatory response to dreaming, snoring or sleep apnea. Ina far field view 50 of R-R interval data, bradycardia manifests as thepresence of a base line heart rate in the range of about 50 bpm 131,coupled with multiple spikes of dots 132 representing intermittentepisodes of elevated heart rate. Such elevations in heart rate during apre-dominantly slower rate may be signs of a cardio-respiratorydisorder. Still other applications of the diagnostic composite plot 80are possible.

The diagnostic composite plots are a tool used by physicians as part ofa continuum of cardiac care provisioning that begins with ECGmonitoring, continues through diagnostic overread and finally, ifmedically appropriate, concludes with cardiac rhythm disorder treatment.Each of these steps involve different physical components thatcollaboratively allow physicians to acquire and visualize R-R intervaland ECG data in a way that accurately depicts heart rate variabilityover time. Further, the data represented by the diagnostic compositeplots can be provided to analysis software for cardiac event detection,and patient diagnosis or confirmation. FIG. 14 is a block diagramshowing a system 140 for facilitating diagnosis of cardiac rhythmdisorders with the aid of a digital computer 150 in accordance with oneembodiment. Each diagnostic composite plot 151 is based on ECG data 166that has either been recorded by a conventional electrocardiograph (notshown) or retrieved or obtained from some other type of ECG monitoringand recording device. Following completion of the ECG monitoring, theECG data is assembled into a diagnostic composite plot 151, which can beused by a physician to diagnosis and, if required, treat a cardiacrhythm disorder, or for other health care or related purposes.

Each diagnostic composite plot 151 is based on ECG data 166 that hasbeen recorded over a period of observation, which can be for just ashort term, such as during a clinic appointment, or over an extendedtime frame of months. ECG recordation and, in some cases, physiologicalmonitoring can be provided through various types of ECG-capablemonitoring ensembles, including a standardized 12-lead ECG setup (notshown), such as used for clinical ECG monitoring, a portable Holter-typeECG recorder for traditional ambulatory ECG monitoring (also not shown),or a wearable ambulatory ECG monitor.

One form of ambulatory ECG monitor 142 particularly suited to monitoringand recording ECG and physiological data employs an electrode patch 143and a removable reusable (or single use) monitor recorder 144, such asdescribed in commonly-assigned U.S. Pat. No. 9,345,414, issued May 24,2016, cited supra, and also, further described in detail below withrespect to FIGS. 15-18. The electrode patch 143 and monitor recorder 144are synergistically optimized to capture electrical signals from thepropagation of low amplitude, relatively low frequency content cardiacaction potentials, particularly the P-waves generated during atrialactivation. The ECG monitor 142 sits centrally (in the midline) on thepatient's chest along the sternum 169 oriented top-to-bottom. The ECGmonitor 142 interfaces to a pair of cutaneous electrodes (not shown) onthe electrode patch 143 that are adhered to the patient's skin along thesternal midline (or immediately to either side of the sternum 169). TheECG monitor 142 has a unique narrow “hourglass”-like shape thatsignificantly improves the ability of the monitor to be comfortably wornby the patient 141 for an extended period of time and to cutaneouslysense cardiac electric signals, particularly the P-wave (or atrialactivity) and, to a lesser extent, the QRS interval signals in the ECGwaveforms indicating ventricular activity.

The electrode patch 143 itself is shaped to conform to the contours ofthe patient's chest approximately centered on the sternal midline. Tocounter the dislodgment due to compressional and torsional forces, alayer of non-irritating adhesive, such as hydrocolloid, is provided atleast partially on the underside, or contact, surface of the electrodepatch, but only on the electrode patch's distal and proximal ends. Tocounter dislodgment due to tensile and torsional forces, a strain reliefcan be defined in the electrode patch's flexible circuit using cutoutspartially extending transversely from each opposite side of the flexiblecircuit and continuing longitudinally towards each other to define in‘S’-shaped pattern. In a further embodiment, the electrode patch 143 ismade from a type of stretchable spunlace fabric. To counter patientbending motions and prevent disadhesion of the electrode patch 143, theoutward-facing aspect of the backing, to which a (non-stretchable)flexible circuit is fixedly attached, stretches at a different rate thanthe backing's skin-facing aspect, where a skin adhesive removablyaffixes the electrode patch 143 to the skin. Each of these componentsare distinctive and allow for comfortable and extended wear, especiallyby women, where breast mobility would otherwise interfere with ECGmonitor use and comfort. Still other forms of ECG monitoring andrecording assembles are possible.

When operated standalone, the monitor recorder 142 senses and recordsthe patient's ECG data 166 and physiological data (not shown) into amemory onboard the monitor recorder 144. The recorded data can bedownloaded using a download station 147, which could be a dedicateddownload station 145 that permits the retrieval of stored ECG data 166and physiological data, if applicable, execution of diagnostics on orprogramming of the monitor recorder 144, or performance of otherfunctions. To facilitate physical connection with the download station145, the monitor recorder 144 has a set of electrical contacts (notshown) that enable the monitor recorder 144 to physically interface to aset of terminals 148. In turn, the download station 145 can be operatedthrough user controls 149 to execute a communications or data downloadprogram 146 (“Download”) or similar program that interacts with themonitor recorder 144 via the physical interface to retrieve the storedECG data 166. The download station 145 could alternatively be a server,personal computer, tablet or handheld computer, smart mobile device, orpurpose-built device designed specific to the task of interfacing with amonitor recorder 144. Still other forms of download station 145 arepossible. In a further embodiment, the ECG data 166 from the monitorrecorder 144 can be offloaded wirelessly.

The ECG data 166 can be retrieved from the download station 145 using acontrol program 157 (“Ctl”) or analogous application executing on apersonal digital computer 156 or other connectable computing device, viaa hard wired link 158, wireless link (not shown), or by physicaltransfer of storage media (not shown). The personal digital computer 156may also execute middleware (not shown) that converts the ECG data 166into a format suitable for use by a third-party post-monitoring analysisprogram. The personal digital computer 156 stores the ECG data 166 alongwith each patient's electronic medical records (EMRs) 165 in the securedatabase 64, as further discussed infra. In a further embodiment, thedownload station 145 is able to directly interface with other devicesover a computer communications network 155, which could be a combinationof local area and wide area networks, including the Internet or anothertelecommunications network, over wired or wireless connections.

A client-server model can be employed for ECG data 166 analysis. In thismodel, a server 62 executes a patient management program 160 (“Mgt”) orsimilar application that accesses the retrieved ECG data 166 and otherinformation in the secure database 164 cataloged with each patient'sEMRs 165. The patients' EMRs can be supplemented with other information(not shown), such as medical history, testing results, and so forth,which can be factored into automated diagnosis and treatment. Thepatient management program 160, or other trusted application, alsomaintains and safeguards the secure database 164 to limit access topatient EMRs 165 to only authorized parties for appropriate medical orother uses, such as mandated by state or federal law, such as under theHealth Insurance Portability and Accountability Act (HIPAA) or per theEuropean Union's Data Protection Directive. Other schemes and safeguardsto protect and maintain the integrity of patient EMRs 165 are possible.

In a further embodiment, the wearable monitor 142 can interoperatewirelessly with other wearable or implantable physiology monitors andactivity sensors 152, such as activity trackers worn on the wrist orbody, and with mobile devices 153, including smart watches andsmartphones. Wearable or implantable physiology monitors and activitysensors 152 encompass a wide range of wirelessly interconnectabledevices that measure or monitor a patient's physiological data, such asheart rate, temperature, blood pressure, respiratory rate, bloodpressure, blood sugar (with or without an appropriate subcutaneousprobe), oxygen saturation, minute ventilation, and so on; physicalstates, such as movement, sleep, footsteps, and the like; andperformance, including calories burned or estimated blood glucose level.Frequently, wearable and implantable physiology monitors and activitysensors 152 are capable of wirelessly interfacing with mobile devices153, particularly smart mobile devices, including so-called“smartphones” and “smart watches,” as well as with personal computersand tablet or handheld computers, to download monitoring data either inreal-time or in batches through an application (“App”) or similarprogram.

Based on the ECG data 166, physicians can rely on the data as medicallycertifiable and are able to directly proceed with diagnosing cardiacrhythm disorders and determining the appropriate course of treatment forthe patient 141, including undertaking further medical interventions asappropriate. The ECG data 166 can be retrieved by a digital computer 150over the network 155. A diagnostic composite plot 151 that includesmultiple temporal points of reference and a plot of R-R interval data isthen constructed based on the ECG data 166, as discussed in detail suprawith reference to FIG. 3, and displayed or, alternatively, printed, foruse by a physician.

In a further embodiment, the server 159 executes a patient diagnosisprogram 161 (“Dx”) or similar application that can evaluate the ECG data166 to form a diagnosis of a cardiac rhythm disorder. The patientdiagnosis program 161 compares and evaluates the ECG data 166 to a setof medical diagnostic criteria 167, from which a diagnostic overread 162(“diagnosis”) is generated. Each diagnostic overread 162 can include oneor more diagnostic findings 168 that can be rated by degree of severity,such as with the automated diagnosis of atrial fibrillation. If at leastone of the diagnostic findings 168 for a patient exceed a thresholdlevel of tolerance, which may be tailored to a specific client, diseaseor medical condition group, or applied to a general patient population,in a still further embodiment, therapeutic treatment (“Therapy”) toaddress diagnosed disorder findings can be generated and, optionally,programmed into a cardiac rhythm therapy delivery device, such as an IMD(not shown), including a pacemaker, implantable cardioverterdefibrillator (ICD), or similar devices.

Facilitating diagnosis of cardiac rhythm disorders first requires thecollection of ECG data from a patient. In one embodiment, the data canbe collected via a wearable ambulatory ECG monitor having an electrodepatch and a monitor recorder. The electrode patch is adhered to thepatient's skin along the sternal midline or immediately to either sideof the sternum to collect the data. A monitor recorder is then snappedinto place on the electrode patch using an electro mechanical dockinginterface to initiate ECG monitoring. FIG. 15 is a perspective viewshowing an ambulatory ECG monitor 212 having an extended wear electrodepatch 215 with a monitor recorder 214 inserted. The body of theelectrode patch 215 is preferably constructed using a flexible backing220 formed as an elongated strip 221 of wrap knit or similar stretchablematerial about 145 mm long and 32 mm at the widest point with a narrowlongitudinal mid-section 223 evenly tapering inward from both sides.However, other lengths and widths are possible. A pair of cut-outs 222between the distal and proximal ends of the electrode patch 15 create anarrow longitudinal midsection 223 or “isthmus” and defines an elongated“hourglass”-like shape, when viewed from above, such as described incommonly-assigned U.S. Design Pat. No. D744659, issued Dec. 1, 2015, thedisclosure of which is incorporated by reference. The upper part of the“hourglass” is sized to allow an electrically non-conductive receptacle225, that sits on top of the outward-facing surface of the electrodepatch 215, to be affixed to the electrode patch 215 with an ECGelectrode placed underneath on the patient-facing underside, or contact,surface of the electrode patch 215; the upper part of the “hourglass”has a longer and wider profile (but still rounded and tapered to fitcomfortably between the breasts) than the lower part of the “hourglass,”which is sized primarily to allow just the placement of an ECG electrodeof appropriate shape and surface area to record the P-wave and the QRSsignals sufficiently given the inter-electrode spacing.

The electrode patch 215 incorporates features that significantly improvewearability, performance, and patient comfort throughout an extendedmonitoring period. The entire electrode patch 215 is lightweight inconstruction, which allows the patch to be resilient to disadhesing orfalling off and, critically, to avoid creating distracting discomfort tothe patient, even when the patient is asleep. In contrast, the weight ofa heavy ECG monitor impedes patient mobility and will cause the monitorto constantly tug downwards and press on the patient's body that cangenerate skin inflammation with frequent adjustments by the patientneeded to maintain comfort.

During every day wear, the electrode patch 215 is subjected to pushing,pulling, and torsional movements, including compressional and torsionalforces when the patient bends forward, or tensile and torsional forceswhen the patient leans backwards. To counter these stress forces, theelectrode patch 215 incorporates crimp and strain reliefs, such asdescribed in commonly-assigned U.S. Pat. No. 9,545,204, issued Jan. 17,2017, the disclosure of which is incorporated by reference. In a stillfurther embodiment, tabs 224 can respectively extend an additional 8 mmto 12 mm beyond the distal and proximal ends of the flexible backing 220to facilitate with adhering the electrode patch 215 to or removing theelectrode patch 215 from the sternum 213. These tabs 224 preferably lackadhesive on the underside, or contact, surface of the electrode patch215. Still other shapes, cut-outs and conformities to the electrodepatch 215 are possible.

The monitor recorder 214 removably and reusably snaps into anelectrically non-conductive receptacle 225 during use. The monitorrecorder 214 contains electronic circuitry for recording and storing thepatient's electrocardiography as sensed via a pair of ECG electrodesprovided on the electrode patch 215. The non-conductive receptacle 225is provided on the top surface of the flexible backing 220 with aretention catch 226 and tension clip 227 molded into the non-conductivereceptacle 225 to conformably receive and securely hold the monitorrecorder 214 in place.

The electrode patch 215 is generally intended for a single use and ismeant to be replaced periodically throughout an extended period ofmonitoring. However, some types of monitoring may not extend over aperiod of time long enough to necessitate replacement of the electrodepatch 215. In those situations, the monitor recorder 214 and electrodepatch 215 can be combined into a single integral assembly.

The monitor recorder 214 includes a sealed housing that snaps into placein the non-conductive receptacle 225. FIG. 16 is a perspective viewshowing the monitor recorder 214 of FIG. 15. The sealed housing 250 ofthe monitor recorder 214 can have a rounded isosceles trapezoidal-likeshape, when viewed from above, such as described in commonly-assignedU.S. Design Pat. No. D717955, issued Nov. 18, 2014, the disclosure ofwhich is incorporated by reference. The edges 251 along the top andbottom surfaces are rounded for patient comfort. The sealed housing 250is approximately 47 mm long, 23 mm wide at the widest point, and 7 mmhigh, excluding a patient-operable tactile-feedback button 255. However,other sizes of the sealed housing are possible.

The sealed housing 250 can be molded out of polycarbonate, ABS, or analloy of those two materials. The button 255 is waterproof and thebutton's top outer surface is molded silicon rubber or similar softpliable material. A retention detent 253 and tension detent 254 aremolded along the edges of the top surface of the housing 250 torespectively engage the retention catch 226 and the tension clip 227molded into non-conductive receptacle 225. Other shapes, features, andconformities of the sealed housing 250 are possible.

The electrode patch 215 is intended to be disposable, while the monitorrecorder 214 is designed for reuse and can be transferred to successiveelectrode patches 215 to ensure continuity of monitoring, if so desired.The monitor recorder 14 can be used only once, but single useeffectively wastes the synergistic benefits provided by the combinationof the disposable electrode patch and reusable monitor recorder. Theplacement of the wearable monitor 212 in a location at the sternalmidline (or immediately to either side of the sternum) benefitslong-term extended wear by removing the requirement that ECG electrodesbe continually placed in the same spots on the skin throughout themonitoring period. Instead, the patient is free to place an electrodepatch 215 anywhere within the general region of the sternum.

As a result, at any point during ECG monitoring, the patient's skin isable to recover from the wearing of an electrode patch 215, whichincreases patient comfort and satisfaction, while the monitor recorder214 ensures ECG monitoring continuity with minimal effort. A monitorrecorder 214 is merely unsnapped from a worn out electrode patch 215,the worn out electrode patch 215 is removed from the skin, a newelectrode patch 215 is adhered to the skin, possibly in a new spotimmediately adjacent to the earlier location, and the same monitorrecorder 214 is snapped into the new electrode patch 215 to reinitiateand continue the ECG monitoring.

During use, the electrode patch 215 is first adhered to the skin in thesternal region. FIG. 17 is a perspective view showing the extended wearelectrode patch 215 of FIG. 15 without a monitor recorder 214 inserted.A flexible circuit 232 is adhered to each end of the flexible backing220. A distal circuit trace 233 from the distal end 230 of the flexiblebacking 220 and a proximal circuit trace (not shown) from the proximalend 231 of the flexible backing 220 electrically couple ECG electrodes(not shown) with a pair of electrical pads 234. In a further embodiment,the distal and proximal circuit traces are replaced with interlaced orsewn-in flexible wires. The electrical pads 234 are provided within amoisture-resistant seal 235 formed on the bottom surface of thenon-conductive receptacle 225. When the monitor recorder 214 is securelyreceived into the non-conductive receptacle 225, that is, snapped intoplace, the electrical pads 234 interface to electrical contacts (notshown) protruding from the bottom surface of the monitor recorder 214.The moisture-resistant seal 235 enables the monitor recorder 214 to beworn at all times, even during showering or other activities that couldexpose the monitor recorder 214 to moisture or adverse conditions.

In addition, a battery compartment 236 is formed on the bottom surfaceof the non-conductive receptacle 225. A pair of battery leads (notshown) from the battery compartment 236 to another pair of theelectrical pads 234 electrically interface the battery to the monitorrecorder 214. The battery contained within the battery compartment 235is a direct current (DC) power cell and can be replaceable, rechargeableor disposable.

The monitor recorder 214 draws power externally from the batteryprovided in the non-conductive receptacle 225, thereby uniquelyobviating the need for the monitor recorder 214 to carry a dedicatedpower source. FIG. 18 is a bottom plan view of the monitor recorder 214of FIG. 15. A cavity 258 is formed on the bottom surface of the sealedhousing 250 to accommodate the upward projection of the batterycompartment 236 from the bottom surface of the non-conductive receptacle225, when the monitor recorder 214 is secured in place on thenon-conductive receptacle 225. A set of electrical contacts 256 protrudefrom the bottom surface of the sealed housing 250 and are arranged inalignment with the electrical pads 234 provided on the bottom surface ofthe non-conductive receptacle 225 to establish electrical connectionsbetween the electrode patch 215 and the monitor recorder 214. Inaddition, a seal coupling 257 circumferentially surrounds the set ofelectrical contacts 256 and securely mates with the moisture-resistantseal 235 formed on the bottom surface of the non-conductive receptacle225. The battery contained within the battery compartment 236 can bereplaceable, rechargeable or disposable. In a further embodiment, theECG sensing circuitry of the monitor recorder 214 can be supplementedwith additional sensors, including an SpO₂ sensor, a blood pressuresensor, a temperature sensor, respiratory rate sensor, a glucose sensor,an air flow sensor, and a volumetric pressure sensor, which can beincorporated directly into the monitor recorder 214 or onto thenon-conductive receptacle 225.

ECG data collected via the ambulatory ECG monitor, such as describedabove, or another monitor can be provided to a post-monitoring analysisprogram for cardiac event detection and patient diagnosis. However,prior to analysis by a program, noise can optionally be removed torefine the ECG data for more accurate detection and diagnosis results.For example, ECG data is often interspersed with events that occur whilerecording and represent device-related events, such as low batterywarnings or reset events, which are not interesting to the analysis ofthe data. Detection and removal of noise not only increases dataquality, but reduces an amount of data necessary for analysis, which canresult in more accurate and quicker results. Additionally, areas ofrecorded data near a period of time when the patient presses thefeedback button tends to look like noise, but is extremely important forconsideration and analysis since the patient believes a cardiac eventmay be occurring.

Noise detection and removal can be performed prior to any processing ofthe ECG data or after some processing has occurred. FIG. 19 is a flowdiagram showing a method 270 for ECG data classification forfacilitating diagnosis of cardiac rhythm disorders with the aid of adigital computer. Cutaneous action potentials of a patient are monitoredand recorded (block 271) as ECG data over a set time period. Uponcompletion of the monitoring period, the ECG and any physiological dataare downloaded or retrieved into a digital computer (block 272).

Some recorded signals within the collected ECG data are low amplitudeand can be incorrectly detected as noise. Optionally, automatic gaincontrol can be applied (block 273) to the recorded signals to place asmany as the ECG signals as possible within a normal range, such as from0.5 to 1.8 mV peak-to-peak, such as described in commonly-assigned U.S.Pat. No. 9,345,414, issued May 24, 2016, the disclosure of which isincorporated by reference. Automatic gain control is generally providedthrough an AGC module that is implemented as part of a suite ofpost-processing modules, although automatic gain control could also beprovided through standalone software or as part of other forms of ECGvisualization and interpretation software. The automatic gain controlcan be run prior to or after noise detection. If prior, gain control isused to regularize the signal amplitude for noise detection; however, ifrun after, gain control is used to control the signal amplitude seen bythe noise detection analysis software. Alternatively, the signalamplitude is controlled manually by a reading technician.

The gain can be determined by defining a temporal window for automaticgain control. The temporal window has to be long enough to capture atleast one heart beat; a five-second temporal window is used, althoughother durations are possible. The peak-to-peak voltage of each ECG datasegment is computed and a single gain factor is applied to the entiresignal such that the average signal falls in the center of the preferredrange. Alternatively, other statistical values could be used torepresent the correlation of the peak-to-peak voltages in the preferredrange, either in lieu of or in addition to the average voltage, such asmaximum or minimum observed voltage, mean voltage, and so on. Each ECGvalue in a segment is then processed in an iterative loop. The ECGvalues are dynamically gained. In one embodiment, the preferred rangefalls from about 2 mV to 10 mV, although other values could be chosen.

Subsequently, noise detection can optionally be performed (block 274) torefine the ECG data for analysis and use in patient diagnosis. The noisedetection can include classifying segments of the ECG data as noise orvalid data, as further described below with reference to FIGS. 19-25. Apotentially-actionable cardiac event within a vector of the ECG data canbe optionally identified and the ECG data during, prior to and after theevent can be selected (block 275), as described supra with respect toFIG. 3. In one example, the vector of ECG data can represent a 40-minute(or other duration) time span that is used in constructing the plot ofR-R interval data, although other pre-event and post-event time spansare possible. The event could be identified with the assistance of asoftware package, such as Holter LX Analysis Software, licensed byNorthEast Monitoring, Inc., Maynard, Mass.; IntelliSpace CardiovascularImage and Information management system, licensed Koninklijke PhilipsN.V., Amsterdam, Netherlands; MoMe System, licensed by InfoBionic,Lowell, Mass.; Pyramis ECG Management, licensed by Mortara InstrumentInc., Milwaukee, Wis.; ICS Clinical Suite, licensed by SpacelabsHealthcare Inc., Snoqualmie, Wash.; or a customized software package.Alternatively, the potentially-actionable cardiac event could beidentified by a physician or technician during review of the ECG data.

Based on the vector of ECG data, a plot of R-R interval data isconstructed (block 276) and can be displayed with both near field andfar field contextual views of the ECG data, as a diagnostic compositeplot, as described supra with respect to FIG. 6. Both views aretemporally keyed to an extended duration R-R interval data view that, inone embodiment, is scaled non-linearly to maximize the visualdifferentiation for frequently-occurring heart rate ranges, such that asingle glance allows the physician to make a diagnosis. All three viewsare presented simultaneously, thereby allowing an interpreting physicianto diagnose rhythm and the pre- and post-contextual events leading up toa cardiac rhythm of interest.

If not previously performed, noise detection can optionally be performed(block 277) on the R-R interval data to remove data segments classifiedas noise, as further described below with respect to FIG. 20, to refinethe data for further analysis. After, a diagnosis of a cardiac rhythmdisorder can optionally be formed (block 278) based on findings madethrough interpretation of heart rate variability patterns in thediagnostic composite plot. For instance, the heart rate variabilitypatterns in the diagnostic composite plot could be provided to a systemthat programmatically detects AF by virtue of looking for the classicGaussian-type distribution on the “cloud” of heart rate variabilityformed in the plot of R-R interval data, which can be corroborated bythe accompanying contextual ECG data. Finally, therapy to addressdiagnosed disorder findings can optionally be programmed into a cardiacrhythm therapy delivery device (step 279), such as an implantablemedical device (IMD) (not shown), including a pacemaker, implantablecardioverter defibrillator (ICD), or similar devices.

A presence of noise in the ECG data can pose problems for cardiologistswhen diagnosing and caring for patients with possible cardiacarrhythmias. In contrast, identifying and removing noise by classifyingsegments of the data can greatly increase accuracy of identifying avalid cardiac event. FIG. 20 is a flow diagram showing a method 290 fornoise detection. Prior to classification of ECG data, an adaptive noisedetector is trained (block 291). In one example, the adaptive noisedetector can be implemented by a convolutional neural network thatrepresents a processing device, such as an algorithm executed by acomputer processor or actual hardware. Other types of systems arepossible.

During training, segments of the ECG data are annotated with aclassification of noise or valid data by software analysis or humanreview. Training is further described in detail below with respect toFIG. 21. Once trained, further ECG data collected from a patient via anambulatory ECG monitor, such as the monitor described above with respectto FIGS. 15-18, is segmented and provided (block 292) to the trainedadaptive noise detector for classification (block 293). However, datafrom other types of ECG monitors can be used.

Classification of the ECG data segments includes assigning a designationof valid data or noise to each segment. Subsequently, the ECG datasegments that correspond with a time frame during which a patientfeedback button on the ECG ambulatory monitor is pressed are identified.Those segments classified as noise and that overlap with the timeframeare trimmed (block 294) to remove the data corresponding with the buttonpress. Defining the time frame is further described in detail below withreference to FIG. 23. Finally, all segments classified as noise can beremoved from the ECG data prior to further processing. In a furtherembodiment, the noise data segments can remain with the ECG data;however, during review by analysis software, those segments classifiedas noise can be ignored.

Accurate classification of the ECG data segments is dependent ontraining accuracy. FIG. 21 is a flow diagram showing, by way of example,a method 300 for training an adaptive noise detector. ECG training datais obtained (block 301) as data files from ambulatory ECG monitorsassociated with a group of patients. The patients can be selectedrandomly or identified based on patient condition. In one embodiment,between 200 and 250 files can be collected from different ECG ambulatorymonitors and used as training data. The ambulatory monitors used tocollect the ECG data can include the monitor described above withrespect to FIGS. 15-18, as well as other types of monitors. The datafile collected from each ECG ambulatory monitor can each include up toor more than 64 MB of data.

The training data is divided (block 302) into segments, and regions ofthe training data are annotated (block 303) with markers for valid dataor noise by software analysis, human review, or a combination ofsoftware analysis and human review. Specifically, each file from one ofthe ambulatory ECG monitors is divided into segments. For example, theECG data from a single file can be divided into over 1500 segments, suchthat each segment includes between 9-10 seconds of data. However, othernumbers of segments and data times represented by each segment arepossible, such as segments of data between 2 and 12 seconds. The datacan be divided into contiguous segments or alternatively, intooverlapping segments. In one embodiment, a neural network views only onesegment at a time for detection and thus, each segment should includeenough data for a determination of noise. The periodicity of heartactivity is an important indicator of non-noise so each segment shouldinclude at least a few beats. However, the larger the segment becomesthe more good signal will be mis-marked as noise on either side of areal noise identification.

Optionally, a portion of the data at the beginning of each trainingsegment can be removed (block 304) since such data often includes orresembles noise, which could lead to misclassification of the data inthat segment. The training data segments are then provided (block 305)to the adaptive noise detector. In one embodiment, the segments of thetraining data are each provided and run through the adaptive noisedetector once. In a further embodiment, the training segments are runmultiple times.

After the training data has been run through the adaptive noisedetector, testing can be performed (block 306) to determineclassification accuracy of the adaptive noise detector. During testing,a set of testing data, such as collected from ECG ambulatory monitors,is run through the trained adaptive noise detector, which classifies thetesting data. Additionally, the testing data is annotated manually orvia a different program with classifications for noise or valid data. Acomparison is made between the annotation of the data and the results ofthe adaptive noise detector. A number of samples correctly classified isdetermined and an average accuracy of the testing data is determined. Inone embodiment, the average accuracy can be determined and reportedafter every n number of batches of ECG data. For example, n canrepresent 100 batches; however, other numbers of batches are possible.The batches can be determined based on an amount of ECG data, a numberof ECG ambulatory monitors providing the data, as well as by othermetrics. In one embodiment, the batch should be small enough to fit inthe memory available, but large enough to amortize startup time. Anaccuracy threshold can be applied to the average accuracy values and ifan average accuracy value or a predetermined number of average accuracyvalues are below the threshold, further training can be performed (block307) to increase the accuracy of the adaptive noise detector.

Once the adaptive noise detector is accurately trained, further ECG datais collected and provided to the detector for classification. FIG. 22 isa flow diagram showing, by way of example, a method 310 forclassification of ECG data. The collected ECG data is segmented intoblocks, such as described above with respect to the training data ofFIG. 21. The ECG data segments are then received (block 311) by theadaptive noise detector, which can be implemented by a convolutionalneural network utilizing, for example, a one dimensional formulation foruse with ECG data. Additionally, the adaptive noise detector can includehidden layers for performing the classification. In the exampledescribed below, two convolutional or pooling hidden layers, and twofully-connected hidden layers are utilized. However, other number oflayers are possible.

During the first convolution layer, ECG trace features are identified(block 312) using, for example, sliding filters. Examples of ECG tracefeatures can include two R waves with three P waves located between theR waves and R wave spikes, as well as other types of features that areindicative of a cardiac event, such as an arrhythmia. The features canbe automatically learned during the training process and take any formthat is learned to be associated with noise or the ECG. In oneembodiment, filters for at least 32 features are run against the ECGdata. During the second convolution layer, repeating patterns of thefeatures are identified (block 313), including, for example, identifyinghigh numbers of successive R wave spikes.

Next, the data obtained from the second convolution layer is provided toa first fully connected cross-connection layer, which builds (block 314)a matrix with the repeating features representing the columns and matrixmultipliers representing rows. An intersection value for eachcombination of the repeating features and matrix multipliers are listedin the matrix as cross connection weights. Specifically, theintersection value can be determined by multiplying each repeatingfeature value with a matrix multiplier and scaling the product by apredetermined factor. However, other methods for determining theintersection values are possible.

The second fully connected cross-connection layer utilizes thecross-connection weights from the first fully connected cross-connectionlayer and multiplies the cross-connection weights by further weights tocalculate (block 315) final cross-connection values for each ECG datasegment. The final cross-connection values include a noiseclassification value and a valid data classification value. The noiseclassification value and the valid data classification value indicate alikelihood that the data segment, respectively, includes noise or doesnot include noise.

Based on the final cross-connection values, a determination is made asto whether the noise classification value for each data segment exceeds(block 316) the valid data classification value, and if so, the noiseclassification is assigned (block 317) to that data segment. Otherwise,if the noise classification value does not exceed (block 316) the validdata classification value, a valid data classification is assigned(block 318) to that data segment. As described above, with respect toFIG. 20, the segments classified as noise are removed from the ECG dataand the remaining valid data segments are provided for further analysisand patient diagnosis.

However, prior to such analysis, patient identified cardiac events areidentified and considered. Patient identified cardiac events areimportant for use in diagnosis since the patient generally providesfeedback when she feels uncomfortable, ill, or is experiencing symptomsresembling a cardiac event. Such feedback is obtained when the patientapplies physical pressure to the feedback button located on the monitorrecorder of the ECG ambulatory monitor, as shown in FIG. 16.

Often times, though, the physical pressure, from the patient, causes thebutton press to appear as noise in the recorded data. Due to theimportance of the data prior to, during, and after the button press, ifthe data collected at the time of the button press is marked as noise,some or all of the related data will be removed from further analysis.Thus, to maintain data related to the button press, segments with datathat overlap a button press event and that are classified as noise aretrimmed to remove the data related to the button from being classifiedas noise.

The trimming can be performed based on defined button press windows.FIG. 23 is a flow diagram showing, by way of example, a method 320 fortrimming data segments classified as noise. Within the ECG data for oneor more ECG ambulatory monitors, presses of the patient feedback buttonare identified (block 321) and marked. For each button press event, abutton window is generated (block 322) by identifying a predeterminedtime before and after each button press event and designating the spanof time as a button window. In one example, the predetermined timesbefore and after a button press event can be the same, such as fiveminutes for a 10 minute window, or a different amount of time. However,in a further embodiment, each of the predetermined time before and aftercan differ.

The data segments classified as noise are identified (block 323) andcompared with the button press windows to further identify (block 324)those noise data segments that overlap a button press window. Theoverlapping noise data segments are trimmed (block 325) to align with astart or end of the button press window, such that the noise datasegment includes no ECG data within the button press window. In oneembodiment, the data removed from the noise data segment can be assigneda valid data classification to ensure the data is later reviewed.Further, a noise classification assigned to any data segment that isfully encompassed by the button window is removed so that the data canbe considered. Finally, the data segments classified as noise areremoved from the ECG data, leaving only the data segments classified asvalid and the data associated with the button presses for furtheranalysis and use in patient diagnosis.

The button press windows can be displayed in a diagnostic composite plotfor analysis by a medical professional. FIG. 24 is a diagram showing, byway of example, a diagnostic composite plot 340 showing data from abutton press window. The diagnostic composite plot 340, as describedabove with respect to FIG. 6, includes an ECG plot presenting a nearfield (short duration) view 51, an ECG plot presenting an intermediatefield (medium duration) view 52, and an R-R interval data plotpresenting a far field (extended duration) view 53. A temporal point ofreference, such as a button press event, is identified and centered onthe x-axis in all three views. The button press event is associated witha button press window 341, which is displayed on the intermediate fieldview. In a further embodiment, all button presses in the ECG datarecording can be displayed. FIG. 25 is a diagram showing, by way ofexample, a summary graph 350, showing instances of button presses. Asummary graph 350 maps all ECG data recorded by the ECG ambulatorymonitor over a period up to seven days. The data is displayed and buttonpresses can be identified via an icon 351. Identifying all instances ofbutton presses over a recording period can be useful to identifypatterns of symptoms experienced by the patient, which can assist inmaking a diagnosis.

Returning to the composite plot 340 of FIG. 24, the placement of asingle button press window in the middle of the x-axes on all threeviews enables the ECG data to be temporally keyed to the R-R intervaldata appearing in the center of the R-R interval data view 53, with anear field view 51 of an ECG displayed at normal (paper-based) recordingspeed and an intermediate field view 52 that presents the ECG dataoccurring before and after the center. As a result, the near field view51 provides the ECG data corresponding to the R-R interval data at thecenter (or another location), while the intermediate field view 52enables presentation of the broader ECG data context going beyond theborders of the near field view 51. In a further embodiment, the centercan be slidably adjusted backwards and forwards in time, with the nearfield view 51 and the far field view 52 of the ECG data automaticallyadjusting accordingly to stay in context with the R-R interval data view51. In a still further embodiment, multiple temporal points of referencecan be identified with each temporal point of reference being optionallyaccompanied by one or more dedicated sets of ECG data views.

The intermediate field view 52 also displays data segments 342, d_(n),which each represent a grouping of ECG data that has been classified asvalid or noise. In one embodiment, the classification for each datasegment 342 can also be displayed. The segments 342 that are assigned anoise classification are compared with the button press window 341.Those noise data segments that overlap with the button press window 341are trimmed. For example, data segment d8 overlaps with the button presswindow 341. Accordingly, the overlapping data 343 at the beginning ofthe button press window is removed from the data segment that isclassified as noise, thus trimming the noise data segment. The trimmeddata can be added to the next consecutive segment or form a new segment.

Subsequently, in one embodiment, a noise threshold can be applied to thedata segments classified as noise to prevent shorts bursts of noisewithin one such segment from requiring all data in that segment to beremoved, since some types of arrhythmias can resemble noise. If the dataidentified as noise within the noise segment occurs over a period thatmeets or exceeds the noise threshold, then that noise data segment isremoved from the ECG data prior to analysis. However, if the dataidentified as noise within the noise data segment occurs over a periodless than the noise threshold, the noise classification can be removedand the data remains with the ECG data. In one embodiment, each segmentis assigned a noise determination of yes or no, with a yes determinationindicating noise is present and a no determination indicating no noiseis present in the segment. The threshold can be applied to one or moresegments and can, in one instance, be equal to the length of apredetermined number of segments. For example, the threshold can be setto 20 seconds based on two 10 second segments of data and can be appliedto multiple consecutive segments of data.

Finally, the data segments classified as noise are removed from the ECGdata, leaving only the valid data segments and data associated with thebutton press windows for further analysis. In one embodiment, such datais provided for software analysis or human review for patient diagnosis,including identification of one or more cardiac events.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope.

What is claimed is:
 1. A system for ECG data classification for use infacilitating diagnosis of cardiac rhythm disorders, comprising: adatabase to store ECG data obtained via an electrocardiography monitorshaped for placement on a patient's chest; and a server comprising acentral processing unit, memory, an input port to receive the ECG datafrom the database, and an output port, wherein the central processingunit is configured to: divide the ECG data into segments; apply noisedetection analysis to the ECG data segments; assign one of a noiseclassification and a valid classification to each segment of ECG data;identify at least one ECG data segment assigned the noise classificationthat includes ECG data that corresponds with feedback from the patientvia the electrocardiography monitor; remove the ECG data thatcorresponds with the patient feedback from the identified ECG datasegment with the noise classification; and removing the ECG datasegments assigned the noise classification from further analysis.
 2. Asystem in accordance with claim 1, wherein the central processing unitassigns the valid classification to the ECG data that corresponds withthe patient feedback.
 3. A system in accordance with claim 1, whereinthe central processing unit removes a portion of the ECG data located ata beginning of each segment prior to performing the noise detectionanalysis.
 4. A system in accordance with claim 1, wherein the centralprocessing unit calculates a noise data classification value and a validdata classification value for each segment during the noise detectionanalysis and performs one or more of: assigning the noise classificationto one of the segments when the noise data classification value exceedsthe valid data classification value; and assigning the validclassification when the noise data classification value fails to exceedthe valid data classification value.
 5. A system in accordance withclaim 1, wherein the central processing unit identifies the ECG datathat corresponds with the patient feedback, comprising: generating awindow by identifying a predetermined time before and after one suchinstance of feedback from the patient; and selecting the ECG data thatcorresponds with the window as the ECG data for removal from theidentified ECG data segment.
 6. A system in accordance with claim 5,wherein the predetermined time before and after the patient feedbackcomprises one of the same time and different times.
 7. A system inaccordance with claim 5, wherein the central processing unit removes thenoise classification from one such segment of ECG data when the windowis the same length as or longer than the segment.
 8. A system inaccordance with claim 5, wherein the patient feedback comprises a pressof a feedback button on the electrocardiography monitor.
 9. A system inaccordance with claim 1, wherein the ECG data segments are one ofoverlapping and contiguous.
 10. A system in accordance with claim 1,wherein the noise detection analysis is performed via a convolutionalneural network.
 11. A method for ECG data classification for use infacilitating diagnosis of cardiac rhythm disorders, comprising:receiving ECG data obtained via an electrocardiography monitor shapedfor placement on a patient's chest; dividing the ECG data into segments;applying noise detection analysis to the ECG data segments; assigningone of a noise classification and a valid classification to each segmentof ECG data; identifying at least one ECG data segment assigned thenoise classification that includes ECG data that corresponds withfeedback from the patient via the electrocardiography monitor; removingthe ECG data that corresponds with the patient feedback from theidentified ECG data segment with the noise classification; and removingthe ECG data segments assigned the noise classification from furtheranalysis.
 12. A method in accordance with claim 11, further comprising:assigning the valid classification to the ECG data that corresponds withthe patient feedback.
 13. A method in accordance with claim 11, furthercomprising: removing a portion of the ECG data located at a beginning ofeach segment prior to performing the noise detection analysis.
 14. Amethod in accordance with claim 11, further comprising: calculating anoise data classification value and a valid data classification valuefor each segment during the noise detection analysis; and performing oneor more of: assigning the noise classification to one of the segmentswhen the noise data classification value exceeds the valid dataclassification value; and assigning the valid classification when thenoise data classification value fails to exceed the valid dataclassification value.
 15. A method in accordance with claim 11, furthercomprising: identifying the ECG data that corresponds with the patientfeedback, comprising: generating a window by identifying a predeterminedtime before and after one such instance of feedback from the patient;and selecting the ECG data that corresponds with the window as the ECGdata associated with the patient feedback.
 16. A method in accordancewith claim 15, wherein the predetermined time before and after thepatient feedback comprises one of the same time and different times. 17.A method in accordance with claim 15, further comprising: removing thenoise classification from one such segment of ECG data when the windowis the same length as or longer than the segment.
 18. A method inaccordance with claim 15, wherein the patient feedback comprises a pressof a feedback button on the electrocardiography monitor.
 19. A method inaccordance with claim 11, wherein the ECG data segments are one ofoverlapping and contiguous.
 20. A method in accordance with claim 11,wherein the noise detection analysis is performed via a convolutionalneural network.